diff --git a/source_for_demo/4_test_faze.py b/source_for_demo/4_test_faze.py
deleted file mode 100755
index 96b4bd9..0000000
--- a/source_for_demo/4_test_faze.py
+++ /dev/null
@@ -1,179 +0,0 @@
-#!/usr/bin/env python3
-
-# --------------------------------------------------------
-# Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
-# NVIDIA Source Code License (1-Way Commercial)
-# Code written by Seonwook Park, Shalini De Mello.
-# --------------------------------------------------------
-
-import os
-import pickle
-import cv2 as cv
-import sys
-
-import numpy as np
-import torch
-from torch.autograd import Variable as V
-import torch.nn.functional as F
-
-#################
-# Configurations
-#################
-ted_parameters_path      = './weights_ted.pth.tar' #'../src/outputs_of_full_train_test_and_plot/checkpoints/at_step_0057101.pth.tar'
-maml_parameters_path     = './weights_maml' #'../src/outputs_of_full_train_test_and_plot/Zg_OLR1e-03_IN5_ILR1e-05_Net64'
-num_finetuning_steps     = 1000
-finetuning_learning_rate = 5e-4
-k                        = 9
-
-##############
-# Setup model
-
-# Set device
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-# Create network
-# WARNING/NOTE: MAKE SURE THIS IS ABSOLUTELY EXACTLY THE SETTING IN WHICH THE
-#               PREDICTIONS WERE GENERATED IN.
-# Create network
-sys.path.append("../src")
-from models import DTED
-network = DTED(
-    growth_rate=32,
-    z_dim_app=64,
-    z_dim_gaze=2,
-    z_dim_head=16,
-    decoder_input_c=32,
-    normalize_3d_codes=True,
-    normalize_3d_codes_axis=1,
-    backprop_gaze_to_encoder=False,
-).to(device)
-
-#################################
-# Load T-ED weights if available
-
-assert os.path.isfile(ted_parameters_path)
-print('> Loading: %s' % ted_parameters_path)
-ted_weights = torch.load(ted_parameters_path)
-if torch.cuda.device_count() == 1:
-    if next(iter(ted_weights.keys())).startswith('module.'):
-        ted_weights = dict([(k[7:], v) for k, v in ted_weights.items()])
-
-#####################################
-# Load MAML MLP weights if available
-
-full_maml_parameters_path = maml_parameters_path + '/%02d.pth.tar' % k  #'/MAML_%02d/meta_learned_parameters.pth.tar' % k
-assert os.path.isfile(full_maml_parameters_path)
-print('> Loading: %s' % full_maml_parameters_path)
-maml_weights = torch.load(full_maml_parameters_path)
-ted_weights.update({  # rename to fit
-    'gaze1.weight': maml_weights['layer01.weights'],
-    'gaze1.bias':   maml_weights['layer01.bias'],
-    'gaze2.weight': maml_weights['layer02.weights'],
-    'gaze2.bias':   maml_weights['layer02.bias'],
-})
-network.load_state_dict(ted_weights)
-
-#####################
-# Example input data
-
-with open('./sample_person_data.pkl', 'rb') as f:
-    data = pickle.load(f)
-
-    def preprocess_image(image):
-        ycrcb = cv.cvtColor(image, cv.COLOR_RGB2YCrCb)
-        ycrcb[:, :, 0] = cv.equalizeHist(ycrcb[:, :, 0])
-        image = cv.cvtColor(ycrcb, cv.COLOR_YCrCb2RGB)
-        image = np.transpose(image, [2, 0, 1])  # Colour image
-        image = 2.0 * image / 255.0 - 1
-        return image
-
-    # Functions to calculate relative rotation matrices for gaze dir. and head pose
-    def R_x(theta):
-        sin_ = np.sin(theta)
-        cos_ = np.cos(theta)
-        return np.array([
-            [1., 0., 0.],
-            [0., cos_, -sin_],
-            [0., sin_, cos_]
-        ]).astype(np.float32)
-
-    def R_y(phi):
-        sin_ = np.sin(phi)
-        cos_ = np.cos(phi)
-        return np.array([
-            [cos_, 0., sin_],
-            [0., 1., 0.],
-            [-sin_, 0., cos_]
-        ]).astype(np.float32)
-
-    def calculate_rotation_matrix(e):
-        return np.matmul(R_y(e[1]), R_x(e[0]))
-
-    n, h, w, c = data['pixels'].shape
-    img = np.zeros((n, c, h, w))
-    R_gaze_a = np.zeros((n, 3, 3))
-    R_head_a = np.zeros((n, 3, 3))
-    for i in range(data['pixels'].shape[0]):
-        img[i, :, :, :] = preprocess_image(data['pixels'][i, :, :, :])
-        R_gaze_a[i, :, :] = calculate_rotation_matrix(data['labels'][i, :2])
-        R_head_a[i, :, :] = calculate_rotation_matrix(data['labels'][i, 2:4])
-
-    # reduce the number of validation samples if
-    # you have less GPU memory
-    num_indices = img.shape[0]
-    train_indices = np.random.permutation(num_indices-200)[:k]
-    valid_indices = np.arange(num_indices-200, num_indices)
-
-    input_dict_train = {
-        'image_a': img[train_indices, :, :, :],
-        'gaze_a': data['labels'][train_indices, :2],
-        'head_a': data['labels'][train_indices, 2:4],
-        'R_gaze_a': R_gaze_a[train_indices, :, :],
-        'R_head_a': R_head_a[train_indices, :, :],
-    }
-
-    input_dict_valid = {
-        'image_a': img[valid_indices, :, :, :],
-        'gaze_a': data['labels'][valid_indices, :2],
-        'head_a': data['labels'][valid_indices, 2:4],
-        'R_gaze_a': R_gaze_a[valid_indices, :, :],
-        'R_head_a': R_head_a[valid_indices, :, :],
-    }
-
-    for d in (input_dict_train, input_dict_valid):
-        for k, v in d.items():
-            d[k] = torch.FloatTensor(v).to(device).detach()
-
-#############
-# Finetuning
-
-from losses import GazeAngularLoss
-loss = GazeAngularLoss()
-
-optimizer = torch.optim.SGD(
-    [p for n, p in network.named_parameters() if n.startswith('gaze')],
-    lr=finetuning_learning_rate,
-)
-
-network.eval()
-output_dict = network(input_dict_valid)
-valid_loss = loss(input_dict_valid, output_dict).cpu()
-print('%04d> , Validation: %.2f' % (0, valid_loss.item()))
-
-for i in range(num_finetuning_steps):
-    # zero the parameter gradient
-    network.train()
-    optimizer.zero_grad()
-
-    # forward + backward + optimize
-    output_dict = network(input_dict_train)
-    train_loss = loss(input_dict_train, output_dict)
-    train_loss.backward()
-    optimizer.step()
-
-    if i % 100 == 99:
-        network.eval()
-        output_dict = network(input_dict_valid)
-        valid_loss = loss(input_dict_valid, output_dict).cpu()
-        print('%04d> Train: %.2f, Validation: %.2f' %
-              (i+1, train_loss.item(), valid_loss.item()))
diff --git a/source_for_demo/requirements.txt b/source_for_demo/requirements.txt
deleted file mode 100644
index 6e11671..0000000
--- a/source_for_demo/requirements.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-apex
-h5py
-imageio
-moviepy
-numpy
-opencv_python
-torch
-torchvision
-tqdm
-yacs
-pandas==0.24.2
-scipy==1.0.0
-hdf5storage
-pgi
-vext
-vext.gi
-
diff --git a/source_for_demo/sample_person_data.pkl b/source_for_demo/sample_person_data.pkl
deleted file mode 100644
index b8d844f..0000000
Binary files a/source_for_demo/sample_person_data.pkl and /dev/null differ